The present disclosure relates to processing ultrasound data. In particular, the processing adapts as a function of one or more surface parameters.
Surface parameters include a normal or other characteristic of a three dimensional surface. Shading is a common way to visualize subtle details of surfaces of an object. In the Lambertian shading model, the surface normal at a point on the surface is the unit vector n, and there is a light source along the unit vector, m. If the intensity of the light source is L, the diffused light reflected by the point, p, on the surface is:R(p)=Ln·m  (1)An image of the surface illuminated by the light source is provided with highlights. During volume rendering, R(p) is added to the luminance-mapped data value computed at a ray location, resulting in a volume rendered image with shading. Such images give a more realistic feel to the 3D representation, especially in the case of a baby's face.
Shaded surface displays have been provided on ultrasound systems. To compute the normal, the voxel-by-voxel intensity gradients are computed along range, azimuth and elevation directions: [Gx, Gy, Gz]. The vector, n, is computed in the acoustic domain by normalizing [Gx, Gy, Gz]. These computations are performed after beamforming and before scan conversion, such as with a central processing unit (CPU). One of the difficulties in generating a shaded surface display of volumetric ultrasound data is the shear amount of computation for the normal, n. Since Gx, Gy and Gz, each represents a separate data volume, the computationally intensive calculations are preformed for three times more data than mere processing a single data set for a volume. The CPU or other device may have limited bandwidth, such as due to processing of other critical tasks. Data transfer may be limited, reducing the likelihood of real time three dimensional imaging.
Other than shading, surface information has been used for angle compensation of Doppler velocity estimates. The surface is used as an indication of flow direction. Adaptive filtering techniques for 2D imaging based on local differential properties of anatomical structures for improving the detectability and image aesthetics have been performed a CPU or specialized hardware. Borders of anatomical structures, such as vessels, the ventricle or baby faces, are detected and segmented. Border detection and segmentation are performed by the CPU, typically at non-real time rates.